A new method of modeling and state of charge estimation of the battery

Liu, Congzhi; Liu, Weiqun; Wang, Lingyan; Hu, Guangdi; Ma, Luping; Ren, Bingyu

doi:10.1016/j.jpowsour.2016.03.112

Cited by 133 publications

(62 citation statements)

References 33 publications

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“…The fractional order Grünwald-Letnikov calculus formulation is given by the following equation [26]:…”

Section: The Definition and Properties Of Fractional-order Calculusmentioning

confidence: 99%

“…The values aim to minimize the error between U i (t) and U f (t) with the performance indicator [26]:…”

Section: Rc Loop Identificationmentioning

confidence: 99%

“…The capacitances and resistances in the model are treated as identical for both the integer and fractional models, because the difference of the integer and fractional model is the orders of the capacitors according to the definition of the fractional capacitor model. According to Equation (24), the orders of C 1 and C 2 are determined by minimizing the performance indicator in Equation (26). From Figure 4, the battery model parameters vary with the value of SOC.…”

Section: Pulse Characterization Experimentsmentioning

confidence: 99%

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A New State of Charge Estimation Algorithm for Lithium-Ion Batteries Based on the Fractional Unscented Kalman Filter

et al. 2017

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An accurate state of charge (SOC) estimation is the basis of the Battery Management System (BMS). In this paper, a new estimation method which considers fractional calculus is proposed to estimate the lithium battery state of charge. Firstly, a modified second-order RC model based on fractional calculus theory is developed to model the lithium battery characteristics. After that, a pulse characterization test is implemented to obtain the battery terminal voltage and current, in which the parameter identification is completed based on least square method. Furthermore, the proposed method based on Fractional Unscented Kalman Filter (FUKF) algorithm is applied to estimate the battery state of charge value in both static and dynamic battery discharging experiment. The experimental results have demonstrated that the proposed method shows high accuracy and efficiency for state of charge estimation and the fractional calculus contributes to the battery state of charge estimation.

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“…The fractional order Grünwald-Letnikov calculus formulation is given by the following equation [26]:…”

Section: The Definition and Properties Of Fractional-order Calculusmentioning

confidence: 99%

“…The values aim to minimize the error between U i (t) and U f (t) with the performance indicator [26]:…”

Section: Rc Loop Identificationmentioning

confidence: 99%

Section: Pulse Characterization Experimentsmentioning

confidence: 99%

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A New State of Charge Estimation Algorithm for Lithium-Ion Batteries Based on the Fractional Unscented Kalman Filter

et al. 2017

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“…15 The performance evaluation of the modular equalization system was also performed, 16 and a new thermal coupling model was built for the state-of-charge estimation. 19 A new modeling method was proposed by Liu et al, 20 and the energy preheating strategy was discussed for the lithium-ion batteries together with its latency temperature. 19 A new modeling method was proposed by Liu et al, 20 and the energy preheating strategy was discussed for the lithium-ion batteries together with its latency temperature.…”

Section: Introductionmentioning

confidence: 99%

A novel weight coefficient calculation method for the real‐time state monitoring of the lithium‐ion battery packs under the complex current variation working conditions

Wang

Fernandez

Xie

et al. 2019

Energy Science & Engineering

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A novel real-time state monitoring method is proposed to realize the real-time energy management of the lithium-ion battery packs, which is conducted in the iterative computational calculation process by introducing an improved weighting factorunscented Kalman filtering algorithm. The accurate state monitoring treatment is investigated by applying a new iterate calculation thought, in which the improved weight coefficient parameter is constructed and its numerical stability is improved.Meanwhile, the recursive calculation is derived by using the real-time measured factors, according to which the state-of-charge estimation is realized accurately. Aiming to adapt the complex current variation working conditions, the nonlinear treatment is introduced to construct the mathematical unscented transforming function. As can be known from the experimental results, the state-of-charge estimation accuracy is 98.34% under the complex current charge-discharge working conditions. Meanwhile, the effective closed-circuit voltage trackage is also investigated accurately and its tracking error is within 3.51% in the complex working conditions, which provides a good security guarantee for the reliable energy supply of the lithium-ion battery packs. K E Y W O R D Scomplex current variation, Kalman filter, lithium-ion battery, state monitoring, unscented transform, weight coefficient | 3039 WANG et Al.

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“…In previous studies, there has already been a variety of battery models. Battery models can be divided into three main types: electrochemical model, empirical model, and equivalent circuit model [13,[22][23][24][25][26]. As battery characteristics change with battery age and temperature variations, some literature has taken the influence of aging and temperature into account in battery models and modeling [27][28][29][30][31].…”

Section: Introductionmentioning

confidence: 99%

Lithium-Ion Battery Online Rapid State-of-Power Estimation under Multiple Constraints

Shun

Huang

et al. 2018

Energies

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Abstract:The paper aims to realize a rapid online estimation of the state-of-power (SOP) with multiple constraints of a lithium-ion battery. Firstly, based on the improved first-order resistance-capacitance (RC) model with one-state hysteresis, a linear state-space battery model is built; then, using the dual extended Kalman filtering (DEKF) method, the battery parameters and states, including open-circuit voltage (OCV), are estimated. Secondly, by employing the estimated OCV as the observed value to build the second dual Kalman filters, the battery SOC is estimated. Thirdly, a novel rapid-calculating peak power/SOP method with multiple constraints is proposed in which, according to the bisection judgment method, the battery's peak state is determined; then, one or two instantaneous peak powers are used to determine the peak power during T seconds. In addition, in the battery operating process, the actual constraint that the battery is under is analyzed specifically. Finally, three simplified versions of the Federal Urban Driving Schedule (SFUDS) with inserted pulse experiments are conducted to verify the effectiveness and accuracy of the proposed online SOP estimation method.

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A new method of modeling and state of charge estimation of the battery

Cited by 133 publications

References 33 publications

A New State of Charge Estimation Algorithm for Lithium-Ion Batteries Based on the Fractional Unscented Kalman Filter

A New State of Charge Estimation Algorithm for Lithium-Ion Batteries Based on the Fractional Unscented Kalman Filter

A novel weight coefficient calculation method for the real‐time state monitoring of the lithium‐ion battery packs under the complex current variation working conditions

Lithium-Ion Battery Online Rapid State-of-Power Estimation under Multiple Constraints

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